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  • Book Overview & Buying R Data Science Essentials
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R Data Science Essentials

R Data Science Essentials

By : Koushik, Kumar Ravindran
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R Data Science Essentials

R Data Science Essentials

3 (3)
By: Koushik, Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
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9
Index

Chapter 5. Developing Regression Models

Regression analysis is a classic example of supervised learning. It's a method that helps you in knowing the relationship between a dependent variable and many other independent variables in the dataset.

The regression models can be broadly classified into logistic and linear models. In the case of logistic regression, the dependent variable is binomial and our output will be a probability of the categorical outcome; a problem of this nature is generally called a classification problem. On the other hand, in linear regression, the dependent variable is continuous in nature and the problems of this nature are called regression problems.

Let's take one example for each classification and regression problem. A typical classification model would be predicting if a banking customer would default his loan using various other details about the customer, such as his demographic, historic, and other details, whereas when we predict how much...

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